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The organizational structure and functional activities of the Spanish Center for Scientific Information and Documentation (CINDOC) are discussed. The library holds 8,500 journals, including all the Spanish scientific journals; 16,000 books and 20 CD-ROM data bases. CINDOC creates and distributes its own data bases that include all the articles published in Spanish scientific journals.
Of the many geophysical means that can be used to probe a planet's interior, seismology remains the most direct. Given that the seismic data gathered on the Moon over 40 years ago revolutionized our understanding of the Moon and are still being used today to produce new insight into the state of the lunar interior, it is no wonder that many future missions, both real and conceptual, plan to take seismometers to other planets. To best facilitate the return of high-quality data from these instruments, as well as to further our understanding of the dynamic processes that modify a planet's interior, various modeling approaches are used to quantify parameters such as the amount and distribution of seismicity, tidal deformation, and seismic structure on and of the terrestrial planets. In addition, recent advances in wavefield modeling have permitted a renewed look at seismic energy transmission and the effects of attenuation and scattering, as well as the presence and effect of a core, on recorded seismograms. In this chapter, we will review these approaches.
Of the many geophysical means that can be used to probe a planet's interior, seismology remains the most direct. In addition to Earth, seismometers have been installed on Venus, Mars, and the Moon. Given that the seismic data gathered on the Moon (now over 40 years ago) revolutionized our understanding of the Moon and are still being used today to produce new insight into the state of the lunar interior, it is no wonder that many future missions, both real and conceptual, plan to take seismometers to other planets. To best facilitate the return of high-quality data from these instruments, as well as to further our understanding of the dynamic processes that modify a planet's interior, various modeling approaches are used to quantify parameters such as the amount and distribution of seismicity, tidal deformation, and seismic structure of the terrestrial planets. In addition, recent advances in wavefield modeling have permitted a renewed look at seismic energy transmission and the effects of attenuation and scattering, as well as the presence and effect of a core, on recorded seismograms. In this talk I will discuss some of these methods and review the history of planetary seismology.
The dusty surfaces of the Moon, Mars, and various asteroids present a significant challenge to NASA’s manned and unmanned space exploration efforts. The fine, electrostatically charged dust is difficult to remove and has resulted in vision obscuration, false instrument readings, contamination, elevated temperatures, performance reduction, and equipment failure. To alleviate these problems, NASA KSC’s Electrostatics and Surface Physics Laboratory (ESPL) is developing active dust mitigation systems for solar system exploration and in situ resource utilization (ISRU). Part of KSC’s Swamp Works system, the lab emphasizes fast-paced, hands-on, cost-effective, and collaborative innovation. The Electrodynamic Dust Shield (EDS) is an ESPL technology being developed to prevent dust accumulation on space components. It consists of a dielectric substrate embedded with parallel electrodes which, when applied with high voltage out-of-phase waveforms, produce a traveling electric field wave that scatters dust particles from its surface via dielectrophoretic and electrostatic forces. The EDS is currently fully functional and has been tested for scalability and endurance in reduced gravity/pressure flight. Next, it will be part of the May 2019 Materials International Space Station Experiment-11 (MISSE-11) payload, which will test small shield samples for performance durability with exposure to the harsh space environment. The summer objective was to test EDS functionality before active and passive dust shield samples launch on MISSE-11, in order to screen for good flight/control samples as well as to compare pre-flight and post-flight clearing efficiencies. I imaged glass and Kapton shields of 2-phase and 3-phase electrode configurations with the Keyence VHX-5000 digital microscope to check for damage before and after all testing for a qualitative pre-flight baseline; helped with vacuum testing of high-voltage shield breakdown; assembled shields and wires using Electrobond 004 silver epoxy; and helped with vacuum and cryogenic dust clearance testing of assembled EDS systems. I learned CAD (computer-aided design) and 3D printing skills to create parts for a shaker table/motor assembly, which will allow for repeatable experiments by evening out EDS dust distribution in a consistent manner prior to dust scattering testing. I will also be improving the interfacing with Alpha Space’s prototype Data Communications Unit (DCU), a payload component to control power to the EDS and store data, by mapping the DCU structure upon power-up and writing shell scripts to improve various processes. Finally, I also CADed and 3D-printed plastic supports for three different geometries of the Electrostatic Precipitator (ESP), a project that aims to use high voltage electrodes to charge and filter out dust from the Martian atmosphere drawn for ISRU devices. This work on dust mitigation technologies will help to support future robotic and human space exploration missions, including NASA’s Journey to Mars; the EDS has especially significant applications to spacecraft solar panels, thermal radiators, viewports, and astronaut visors. This opportunity has also allowed me to become more familiar with various lab equipment and hardware testing, develop key modeling skills in SolidWorks, explore past and present components critical to America’s space program, work with inspiring peers and mentors, and ascertain my dream of working permanently for NASA.
Cyberphysical systems with autonomous functions are complex pieces of software, consisting of many components, some of which implement autonomous functionality and some may use AI or machine learning algorithms. Software bugs in an autonomous system are of particular concern, as they can have catastrophic consequences. However, detailed studies based on empirical data are rare and therefore these bugs are not well understood. This paper aims to contribute towards filling that gap by investigating the software changes and bugs in Autonomy Operating System (AOS) for Unmanned Aircraft Systems (UAS), which consist of 26 components containing about 103,000 lines of code and having a total of 772 bugfixes. Based on the data extracted from the code repository and semi-structured interviews with the developers of AOS, we explore the differences among autonomous software components, components developed using Model-based Software Engineering, and reuse with respect to change proneness, fault proneness, distribution of bugfixes among AOS components and files of these components, and characteristics of bugs of different AOS components. Our results show that the autonomous components were significantly more change prone (measured in number of commits and code churn) and fault prone (measured in bugfixes per KLoC) than non-autonomous components. The distribution of the locations of bugfixes was skewed, both at component and file level (i.e., a small number of components / files contained the majority of bugs). These evidence-based findings provide important insights to researchers and practitioners alike and can be used to efficiently improve the quality and reliability of autonomous systems.
Traditionally, High Energy data analysis is based on the model where data are stored in files and analyzed by running multiple analysis processes, each reading one or more of the data files. This process involves repeated data reduction step, which produces smaller files, which is time consuming and leads to data duplication. We propose an alternative approach to data storage and analysis, based on the Big Data technologies. The idea is to store each element of data once and only once in a distributed scalable database and analyze data by reading only "interesting" pieces of data from the database. To make this approach possible, we developed columnar Striped Data Representation Format as the basis of the framework. Traditional columnar approach allows for efficient analysis of complex data structures. While keeping all the benefits of columnar data representation, striped mechanism goes further by enabling efficient parallelization of computations and flexible dist ribution of data analysis. The framework includes scalable and elastic data storage, compute and user analysis backend components. The framework uses off-the shelf web services and data caching technologies as the compute/data co-location mechanism. Flexible architecture allows the framework run in the cloud using container technologies. The framework offers Python/Jupyter as the user analysis backend platform, but can also run in command line or batch mode. In the article, we will present the results of the FNAL-LDRD-2016-032 FNAL LDRD project, the design, implementation, features and performance characteristics of the Striped Data Analysis Framework.
Damage diagnosis in critical components is essential for ensuring the safety and reliability of operations across industries, spanning manufacturing, aerospace, and energy. Traditional acoustic nondestructive testing methods primarily focus on detecting defects through the direct scattering of single-mode incident waves from the damage, which limit their applicability to simple structures and small inspection areas. Our earlier research demonstrated that machine learning algorithms combined with sparse sensor networks can identify critical defect signatures even from multiply scattered, multi-mode acoustic signals, indicating the potential for improved defect inspection in complex, real-world structures. In this work, we demonstrate the successful implementation of this approach in a fixed sensor configuration to rapidly and accurately detect simulated defects in a geometrically complex, real-world structure, a brake rotor hub. Three different types of defects were physically simulated on the surface of the hub, and the collected data were used to train an autoencoder-based deep learning model. Two models were tested, one using single measurements and the other using multiple measurements taking advantage of the spatial distribution of the sensor network. After training, the multi-measurement model achieved 100 % accuracy in identifying, classifying, and locating unseen, unique damages. This work illustrates the potential of the proposed method for a wide range of industrial applications.
The Laser Vegetation Imaging Sensor (LVIS) is an airborne, wide-swath, digitization-only laser altimeter capable of collecting full return waveforms (i.e. echoes) from laser footprints ranging in diameter from 1 to 80 m across up to a 1 km wide data swath. The return waveform can be used to enhance the accuracy of laser ranging and to provide information about the vertical structure of vegetation and topography within each laser footprint. Although extremely small laser footprints (< 1 0 cm diameter) generally return simple, impulse responses to their target surface, larger footprints typically exhibit complex returns representing the diverse vertical distributions of surfaces contained in each footprint. Only a handful of airborne and spaceborne laser altimeters record the return echo or return pulse that is reflected from the Earth's surface (i.e. NASA's LVIS, SLA, VCL, and GLAS laser altimeters). Waveforms are currently interpreted to extract a timing or ranging point or points to represent the mean ground elevation or the vertical height of vegetation. But, recent progress using pulse shape correlation techniques shows promise for a variety of science applications involving change detection of surface topography and vegetation as well as potential for improving data processing by correlating images or crossovers to solve systematic biases. We show example correlation images from LVIS and discuss instrument design implications and potential science applications.
X-ray absorption measurements at the U L III , Ru K , and Fe K edges are reported for the hidden order (HO) material URu 2 - x Fe x Si 2 ( x = 0 , 0.05, 0.08, 0.10, 0.12, 0.15, and 0.20) as a function of x and temperature T . Furthermore, when Fe is substituted for Ru, the local structure about Fe shrinks slightly and the first neighbor Fe-Si bond length decreases by ≈ 0.05 Å . More importantly excess disorder is observed below 80–100 K (the coherence temperature T * ) in plots of the Debye-Waller factor σ 2 ( σ is the width of the pair distribution function); at low T the data deviate from the usual Einstein or correlated-Debye model plots. This excess disorder is most prominent for the Ru-Si bond, and σ 2 actually increases below 80 K. These results suggest a local orthorhombic distortion with B 1 g -like symmetry that develops below 80–100 K. A model that describes these local distortions is presented, and discussed in terms of other measurements that indicate a breaking of fourfold symmetry at low T . In addition, the square root of the difference between σ 2 ( T ) for the Ru-Si pair and a Debye fit to these data serves as an order parameter for this orthorhombic distortion, in the temperature range below 100 K. This quantity is a length related to a - b , the difference between the a and b lattice constants in the orthorhombic phase, and provides a connection between this distortion and T * . X-ray absorption near edge structure (XANES) measurements also show that there are no changes in the edge positions down to 0.1 eV for any edge as a function of x , for T in the HO regime.
During August 2006, the NASA African Multidisciplinary Analyses Mission (NAMMA) field experiment was conducted to characterize the structure of African Easterly Waves and their evolution into tropical storms. Mineral dust aerosols affect tropical storm development, although their exact role remains to be understood. To better understand the role of dust on tropical cyclogenesis, we have implemented a dust source, transport, and optical model in the NASA Goddard Earth Observing System (GEOS) atmospheric general circulation model and data assimilation system. Our dust source scheme is more physically based scheme than previous incarnations of the model, and we introduce improved dust optical and microphysical processes through inclusion of a detailed microphysical scheme. Here we use A-Train observations from MODIS, OMI, and CALIPSO with NAMMA DC-8 flight data to evaluate the simulated dust distributions and microphysical properties. Our goal is to synthesize the multi-spectral observations from the A-Train sensors to arrive at a consistent set of optical properties for the dust aerosols suitable for direct forcing calculations.
Binary systems with an accreting compact object offer a unique opportunity to investigate the strong, clumpy, line-driven winds of early-type supergiants by using the compact object's X-rays to probe the wind structure. We analyze the two-component wind of HDE 226868, the O9.7Iab giant companion of the black hole Cyg X-1, using 4.77 Ms Rossi X-ray Timing Explorer (RXTE) observations of the system taken over the course of 16 years. Absorption changes strongly over the 5.6 d binary orbit, but also shows a large scatter at a given orbital phase, especially at superior conjunction. The orbital variability is most prominent when the black hole is in the hard X-ray state. Our data are poorer for the intermediate and soft state, but show signs for orbital variability of the absorption column in the intermediate state. We quantitatively compare the data in the hard state to a toy model of a focussed Castor-Abbott- Klein wind: as it does not incorporate clumping, the model does not describe the observations well. A qualitative comparison to a simplified simulation of clumpy winds with spherical clumps shows good agreement in the distribution of the equivalent hydrogen column density for models with a porosity length on the order of the stellar radius at inferior conjunction; we conjecture that the deviations between data and model at superior conjunction could either be due to lack of a focussed wind component in the model or to a more complicated clump structure.
A method for developing models of semiconductor devices with two-dimensional nonuniform concentration profiles of donors and acceptors in working regions of a semiconductor device structure is for the first time proposed based on a complex of physical-topological modeling of charge carrier transport and process simulation of the forming processes of the device structure. The application of process simulation is due to the need to correctly define the parameters of the semiconductor device structure, which are used as initial data to calculate the electron transport according to the physical-topological model. The parameters of production processes of ion implantation, diffusion, and lithography, which were refined during process simulation, are determined for a high-power metal—oxide—semiconductor (MOS) transistor forming by the double diffusion method in accordance with known electrical characteristics and measured geometrical sizes of the structure. This results in two-dimensional distribution profiles of donors and acceptors in p–n junctions necessary to calculate the transistor breakdown under the effect of pulsed γ radiation. Breakdown processes are modeled with the help of the physical-topological model based on the Poisson and continuity equations as well as expressions for the diffusion and drift current densities in the transistor. Accounting for carriers formed at the instant of γ irradiation is implemented by the introduction of the dependence of the generation coefficient of electron–hole pairs on the radiation-dose power. The results of calculations correlate well with the experimental data, which makes it possible to give a conclusion regarding the adequacy of the proposed complex model.
Rotational modulations of emission spectra in brown dwarf and exoplanet atmospheres show that clouds are often distributed non-uniformly in these ultracool atmospheres. The spatial heterogeneity in cloud distribution demonstrates the impact of atmospheric dynamics on cloud formation and evolution. In this study, we update the Hubble Space Telescope (HST) time-series data analysis of the previously reported rotational modulations of WISEP J004701+680352—an unusually red late-L brown dwarf with a spectrum similar to that of the directly imaged planet HR 8799e. We construct a self-consistent spatially heterogeneous cloud model to explain the HST and the Spitzer time-series observations, as well as the time-averaged spectra of WISE 0047. Additionally, in the heterogeneous cloud model, a cloud thickness variation of around one pressure scale height explains the wavelength dependence in the HST near-IR spectral variability. By including disequilibrium CO/CH 4 chemistry, our models also reproduce the redder $J-{K}_{{\rm{s}}}$ color of WISE 0047 compared to that of field brown dwarfs. We discuss the impact of vertical cloud structure on atmospheric profile and estimate the minimum eddy diffusivity coefficient for other objects with redder colors. Our data analysis and forward modeling results demonstrate that time-series spectrophotometry with a broad wavelength coverage is a powerful tool for constraining heterogeneous atmospheric structure.
The NOW (Nanosatellite Orbital Workshop) of NASA Ames Research Center (ARC) has two cubesats in orbit at this time: 6 U TechEdSat-10 (T-10) and the 3U TechEdSat-7 (T-7). T10 was jettisoned from the ISS via the NANORACKS system 7/13/2020, and T-7 was launched via Virgin Orbit 1/17/2021. Both were built by the Nano-satellite Orbital Workshop (NOW) at NASA ARC, and designed and fabricated by interns and students in collaboration with educational institutions. Prototyping novel technologies for non-powered re-entry and communications from orbit are primary research interests, however all subsystems including power generation and distribution, subsystem control, navigation, positioning, heat management etc. extend current technologies. Use of distributed processors using open software platforms and standards other based technologies and software is integral to all segments of spacecraft design. Here, we will present an overview of the spacecraft, experiments, and accomplishments – as well as the next three flight experiments. Some of these experiments include: The exo-brake re-entry system is being developed to enable sample return and end of life disposal; Internal communications for sensors, inter-subsystem and experiments uses both a Zigbee based PAN and internal Wi-Fi for high-speed inter-device communications; The Iridium small message LEO system (Short Burst Data) is used to both command the spacecraft and send data to the ground; Experimental use of the Global-Star system for L-band system comparison and back-up; Collaborative NOAA an experiment to communicate from LEO to the GOES geostationary satellite using the DCS (Data Collection System) with on-board Doppler correction; Mars and Lunar experimental communication systems for future cis-lunar and interplanetary nano-satellites; First demonstration of the NASA Near Earth Network systems with nano-satellites at NASA/Wallops Island; Solar array design and implementation for unique future flexible structures; Power distribution using Tardigrade rad-hard processor omni-board (designed by the team); Distributed processors with internal Wi-Fi connectivity; and Initial experiments with AI/Machine Learning.
The flow conditions for which heating distributions were measured on corrugated surfaces and wavy walls in turbulent boundary layers are shown, along with the ratio of the displacement thickness to the roughness height versus the local edge Mach number for an equivalent smooth surface. The present data are seen to greatly extend the range of data available on corrugated surfaces in turbulent boundary layers. These data were obtained by testing fullscale corrugation roughened panels in the wall boundary layer of a supersonic and hypersonic wind tunnel. The experimental program used to obtain the data is described. The data are analyzed and correlated in terms of the pertinent flow and geometric parameters. The developed correlations are compared with the available thin boundary layer data, as well as with previously published correlation techniques.
Using published data from the Jovian magnetopause crossings of Pioneers 10 and 11, the apparent crossing times as determined from the solar wind plasma detector and from the vector magnetometer are comapred. There were 13 published crossings for which both plasma and magnetic-field signatures were clearly defined. The occurrence distribution of these 13 crossings with respect to the relative timing of plasma and field signatures is shown. Results indicate that Jupiter's magnetopause is more turbulent than the Earth's possibly as the result of the large velocity shear between the magnetosheath and the partially corotating Jovian plasma. However, it is noted that the data set considered is too small to justify a definitive interpretation.
Data from the Viking lander imaging experiment and the IRIS experiment on board the Mariner 9 Orbiter are used to develop a one-dimensional radiative-convective model to describe the Mars temperature profile. Photography was employed to determine the optical depth, mean size, shape, visible absorption coefficient, and vertical distribution of aerosols in the Mars atmosphere. The calculation procedures are outlined, particularly for the particle size distribution function, which indicated a cross-sectional average particle radius of 2.5 microns. Dust-free conditions in the atmosphere near the equator and at lower altitudes permit full surface absorption of sunlight and subsequent adiabatic lapse rates. Difficulties were encountered in accurately modeling temperature profiles to fit Viking lander data, and resultant variations are attributed to effects of both suspended dust particles and large scale circulation.
Grain growth in the wake of the melt pool formed during alloy-based additive manufacturing (AM) is complex and multifaceted, depending on parameters governing heat transport, fluid flow, and solidification itself. Cellular automata (CA) models have proven effective in providing computationally efficient and physically sound predictions of grain structure for several AM problems, but their efficiency is tied to the performance of heat transport models. CA models use only a small portion of the problem's temperature data (near the moving melt pool boundary), and much of the CA calculations do not affect the final result due to re-melting of material. Coupling of and communication between heat transport and solidification models, and eliminating operations irrelevant towards final grain structure prediction, will be necessary for using these methods for efficient simulation of large parts. In this work, we introduce a procedure of decoupling the CA from temperature field simulation, using files of relevant temperature data written by the heat transport model. This approach is validated against the standard coupling approach using data obtained through the computational fluid dynamics software OpenFOAM. Negligible differences are seen in grain size, volume, and texture distributions for multilayer simulation of test problems, while the quantity of temperature data for these test problems was reduced by four orders of magnitude (from 100s of GB to 10s of MB) and the code performance sped up by a factor of around 50. Furthermore, variability in microstructure as a function of cell size, substrate, time step, and nucleation parameters is studied, and it is found that cell sizes less than or equal to 1.67 μm and sufficiently small time steps yield Αstatistically equivalent microstructures. Finally, a potential use case for this CA approach—the layer-wise convergence in grain structure starting from extremes in initial grain size—is examined. This approach's ability to simulate expected trends in nucleation and epitaxial grain growth for large regions of microstructure, simulated independently of heat transport models themselves, should prove useful for investigation of various microstructure uncertainties and prediction of part-scale experimental results.